QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models

TL;DR

QUEENS is an open-source Python framework that streamlines the systematic analysis of large-scale computational models using diverse solvers and distributed systems, integrating advanced algorithms for uncertainty quantification and Bayesian analysis.

Contribution

It introduces a modular, solver-independent framework that combines simulation management with state-of-the-art algorithms for uncertainty quantification and Bayesian inverse analysis in a scalable manner.

Findings

Supports a wide range of analysis types with modular architecture

Includes advanced algorithms for uncertainty quantification and Bayesian analysis

Facilitates scalable, distributed computing for large-scale models

Abstract

A growing challenge in research and industrial engineering applications is the need for repeated, systematic analysis of large-scale computational models, for example, patient-specific digital twins of diseased human organs: The analysis requires efficient implementation, data, resource management, and parallelization, possibly on distributed systems. To tackle these challenges and save many researchers from annoying, time-consuming tasks, we present QUEENS (Quantification of Uncertain Effects in Engineering Systems), an open-source Python framework for composing and managing simulation analyses with arbitrary (physics-based) solvers on distributed computing infrastructures. Besides simulation management capabilities, QUEENS offers a comprehensive collection of efficiently implemented state-of-the-art algorithms ranging from routines for convergence studies and common optimization algorithms to more advanced sampling algorithms for uncertainty quantification and Bayesian inverse analysis. Additionally, we provide our latest cutting-edge research in multi-fidelity uncertainty quantification, efficient multi-fidelity Bayesian inverse analysis, and probabilistic machine learning. QUEENS adopts a Bayesian, probabilistic mindset but equally supports standard deterministic analysis without requiring prior knowledge of probability theory. The modular architecture allows rapid switching between common types of analyses and facilitates building sophisticated hierarchical algorithms. Encouraging natural incremental steps and scaling towards complexity allows researchers to consider the big picture while building towards it through smaller, manageable steps. The open-source repository is available at https://github.com/queens-py/queens.